Flow generation: Low code gets conversational
Of course, understanding users’ requests is no use if the system can’t do anything about them.
“People have to create these workflows in order to … have something for the virtual agent to actually trigger in order to resolve the issue,” Barnes said. “One of the back-end bottlenecks for our customers is, they just don’t have enough ServiceNow developers to create the flows.”
So ServiceNow is applying generative AI (this time without the Now Assist label on it) to flow generation, using it to convert plain text to low-code automated workflows.
Users describe the workflow they want, and the system will translate it into a flow in the visual editor, drawing on ServiceNow’s best practices and the customer’s own coding style.
“This just makes it massively easier, and doesn’t require as much upskilling to get it right,” Barnes said, adding that users (or their IT staff) will still have to verify the code for themselves, using the platform’s existing automated testing tools.
The hard part: Integrating for business value
Element AI was set up to build AI applications that could be easily integrated into business processes. But despite the complexities of building and training generative AI systems, Barnes said it was something of a relief when the company was acquired because “a lot of the really hard stuff we would have had to do, building a workflow engine, we didn’t have to do any more” because “it was already done by ServiceNow.”
He encouraged CIOs to consider that challenge too, as they weigh which of the growing number of generative AI chatbot building tools to use.
“For any generative AI functionality, if it’s going to go beyond having a nice conversation, … it needs the power of a platform that can take action underneath it,” he said.
